Background: Schools face increasing demands to provide education on healthy living and improve core academic performance. Although these appear to be competing concerns, they may interact beneficially. This article focuses on school garden programs and their effects on students' academic and dietary outcomes.
Methods: Database searches in CABI, Web of Science, Web of Knowledge, PubMed, Education Full Text, Education Resources Information Center (ERIC), and PsychINFO were conducted through May 2013 for peer-reviewed literature related to school-day garden interventions with measures of dietary and/or academic outcomes.
Results: Among 12 identified garden studies with dietary measures, all showed increases/improvements in predictors of fruit and vegetable (FV) consumption. Seven of these also included self-reported FV intake with 5 showing an increase and 2 showing no change. Four additional interventions that included a garden component measured academic outcomes; of these, 2 showed improvements in science achievement and 1 measured and showed improvements in math scores.
Conclusions: This small set of studies offers evidence that garden-based learning does not negatively impact academic performance or FV consumption and may favorably impact both. Additional studies with more robust experimental designs and outcome measures are necessary to understand the effects of experiential garden-based learning on children's academic and dietary outcomes.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/josh.12278 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The impact of COVID-19 pre-university education on first-grade medical students. A performance study of students of a Department of Histology.
Anat Sci Educ
January 2025
Tissue Engineering Group, Department of Histology, Medical School, University of Granada, Granada, Spain.
The recent coronavirus disease (COVID-19) forced pre-university professionals to modify the educational system. This work aimed to determine the effects of pandemic situation on students' access to medical studies by comparing the performance of medical students. We evaluated the performance of students enrolled in a subject taught in the first semester of the medical curriculum in two pre-pandemic academic years (PRE), two post-pandemic years (POST), and an intermediate year (INT) using the results of a final multiple-choice exam.
View Article and Find Full Text PDFEvaluating Proton Versus Photon Therapy: A Call for Nuanced Decision-Making.
J Med Radiat Sci
January 2025
Royal Adelaide Hospital, Adelaide, South Australia, Australia.
This letter critically evaluates the conclusions drawn by Li et al. (https://doi.org/10.
View Article and Find Full Text PDFReview of Short-Wavelength Infrared Flip-Chip Bump Bonding Process Technology.
Sensors (Basel)
January 2025
Research and Development Center of Optoelectronic Hybrid IC, Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China.
Short-wave infrared (SWIR) imaging has a wide range of applications in civil and military fields. Over the past two decades, significant efforts have been devoted to developing high-resolution, high-sensitivity, and cost-effective SWIR sensors covering the spectral range from 0.9 μm to 3 μm.
View Article and Find Full Text PDFScale-Consistent and Temporally Ensembled Unsupervised Domain Adaptation for Object Detection.
Sensors (Basel)
January 2025
School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Unsupervised Domain Adaptation for Object Detection (UDA-OD) aims to adapt a model trained on a labeled source domain to an unlabeled target domain, addressing challenges posed by domain shifts. However, existing methods often face significant challenges, particularly in detecting small objects and over-relying on classification confidence for pseudo-label selection, which often leads to inaccurate bounding box localization. To address these issues, we propose a novel UDA-OD framework that leverages scale consistency (SC) and Temporal Ensemble Pseudo-Label Selection (TEPLS) to enhance cross-domain robustness and detection performance.
View Article and Find Full Text PDFEmpowering the Sports Scientist with Artificial Intelligence in Training, Performance, and Health Management.
Sensors (Basel)
December 2024
Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, CreativeLab Research Community, 5000-801 Vila Real, Portugal.
Artificial Intelligence (AI) is transforming the field of sports science by providing unprecedented insights and tools that enhance training, performance, and health management. This work examines how AI is advancing the role of sports scientists, particularly in team sports environments, by improving training load management, sports performance, and player well-being. It explores key dimensions such as load optimization, injury prevention and return-to-play, sports performance, talent identification and scouting, off-training behavior, sleep quality, and menstrual cycle management.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!